Background: At the present time, various technologies have been used in the development of modern agriculture. The technology used to help in the development of agricultural farms is called a smart farm. Mashed Mushroom is a food due to their usefulness and high nutritional and good income, as this result, the researcher was interested in the use of technology and innovation of smart farms in the mashed mushroom cultivation house and find a mathematical model of the optimum temperature and humidity for growing mashed mushrooms. Furthermore, find a mathematical model of the yield amount of mashed mushrooms against temperature and humidity. 
 
Methods: There are 5 methods in this research, to be composed of studying the problem, planning the operation process, using technology and innovation in smart farms, collecting data and finally finding a mathematical model of the optimum temperature and humidity for growing mashed mushrooms. In addition, a mathematical model of the yield of mashed mushrooms was obtained.&nbsp; 
 
Result: The results showed that the mathematical model of the optimum temperature for mashed mushroom was T= 0.3267t3-2.83t2+7.1433t+20, the most appropriate temperature was 24.95&plusmn;5.557&deg;C at the average andthe mathematical model of the optimum humidity for mashed mushroom was H= -0.8417t3+4.82-0t2.1883t+59.65, the most appropriate humidity was 74.58&plusmn;15.451% at the average. In addition, the mathematical model of product yield of mashed mushrooms was&nbsp;<img alt="" src="data:image/png;base64,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" />= -1.479+0.055T+0.028H when temperature T and humidity H were determined.

Mathematical Model for Optimum Temperature and Humidity Determination of Mashed Mushroom in Greenhouses by using Smart Farm Technology and Innovation

<div>Respiration rates (RR) have been used as an index for the metabolic activities of Mango during ripening and senescence. A knowledge on respiration rate will be very much needed for enhanced shelf life of Mango particularly during Modified Atmosphere Storage. Hence a work was carried out to determine respiration rates of three varities of mango (Magnifera indica) viz., Malgoa, Banganapalli and Neelum stored at three different tempertautres (Ambient, 24&deg;C and 14&deg;C). Known weight of fruits were kept under air tight condition in the plastic container fitted with a silicon septum. Every day three gas samples of 5 ml volume were drawn from the chamber through silicon rubber septum using a needle and the oxygen concentration was found out using MAP analyzer. The oxygen concentration was determined for 13 days of storage at all temperatures. The respiration rates were determined using experimentally and the values were substituted in formulae method for prediction. The value of the constants were determined using non-linear regression using Sigmaplot 8.0 software. The development of the mathematical model for the prediction of Respiration Rates were found to be useful for further reference.</div>

<div>Mathematical Modeling of Respiration Rate of Mango (Magnifera indica)&nbsp;</div>

<div style="text-align:justify">Background: Papaya leaves botanically classified as Carica papaya and are used medicinally in teas to aid in digestion. The Carica Papaya leaves are often considered as wastes, but they are actually beneficial for health. The study was aimed to achieve the suitable drying parameters of operation that makes the drying process to complete in less time without effecting the keeping quality of the product.&nbsp; 
 
Methods: The study was conducted using 3 drying methods which consisted of tray drying at temperatures 50&deg;C, 60&deg;C and 70&deg;C, microwave drying at power levels 20% (180 Watts), 30% (270 Watts) and 40% (360 Watts), microwave vacuum drying at pressures of vacuum 400 mmHg, 500 mmHg and 600 mmHg and power levels 20% (180 Watts), 30% (270 Watts) and 40% (360 Watts). Twelve different mathematical drying models were compared based on their correlation coefficient, root mean square error and reduced chi-square to estimate the drying curves. 
 
Result: Experimental results showed that page model and weibulls distribution gave the best results, evidenced by the highest average correlation coefficient value of 0.9796, lowest average RMSE (Root mean square error) value of 0.0286 lowest average SSE value of 0.014 and the reduced average c&sup2; value of 0.0019. It is concluded that page model and weibulls distribution could sufficiently describe the tray drying, microwave and microwave vacuum drying respectively of carica papaya leaves.</div>

Mathematical Modelling of Dried Carica papaya Leaves

In the recent past, the global fruit industry has experienced incredible growth, fueled by growing per capita earnings and greater health consciousness for fresh produce. Fruit volume plays a key role in precise yield estimation, improving productivity, sorting and packaging. This systematic review accompanied by meta-analysis sheds light on the non-destructive techniques and algorithms used in the estimation of fruit volume through mathematical modeling.&nbsp; A total of 50 studies published between 2008 and 2023 were reviewed in this work in 2023 at Shivaji College (University of Delhi), Delhi. Reviewing the studies analytically, the modeling techniques adopted by researchers usually belonged to categories of either statistical modeling or geometric modeling. An I-square statistic of 88.48% was obtained in the heterogeneity analysis demonstrating the extreme diversity between the above categories. Egger&rsquo;s and Begg&rsquo;s tests were also performed for examining the presence of publication bias, however they did not turn up any compelling evidence of its occurrence. The comparison between different categories with their coefficient of determination (R2) between estimated and actual volume was also established using effect measures like odds ratios, risk ratiosand weighted odds ratios while sensitivity analysis was performed to assess the changes in result. This study also elucidates the strengths and shortcomings of different non-destructive techniques while using statistical methods to identify the performance of individual studies and to find the most suitable approach for estimating fruit volume. The meta-analysis concluded that the studies following statistical approach offered better R2&nbsp; values as compared to other methodologies.

Non-destructive Mathematical Modeling Techniques for Fruit Volume Estimation: A Systematic Review and Meta-analysis

Background: BT-Black Turmeric (Curcuma caesia), is a medicinal plant rich in bioactive compounds. Drying extends its shelf life while preserving phytochemicals, but the method and parameters impact drying rate and quality. Optimizing drying conditions is crucial for efficient, high-quality industrial processing. 
 
Methods: This study investigates the drying kinetics of BT slices at three thickness levels (1 mm, 2 mm and 3 mm) under different drying techniques: hot air oven drying (HAO), tray drying (TD) and vacuum drying (VD). Drying experiments were conducted at temperatures of 60&deg;C, 70&deg;C and 80&deg;C to assess the influence of temperature and slice thickness on drying efficiency. The moisture ratio was modelled using nine mathematical models to determine the most suitable model for predicting drying behavior. Model performance was evaluated based on the coefficient of determination (R2). 
 
Result: The experimental results indicate that 1 mm slices exhibited the highest drying rate across all drying techniques and temperatures. Among the drying methods, TD was identified as the most effective at 80&deg;C, 1 mm slices in TD demonstrated significantly reduced drying time, minimal color degradation providing an optimal balance between drying kinetics and product quality, making it the most favourable condition for large-scale processing. Furthermore, the Midilli model exhibited the best fit for describing the drying behavior of BT, achieving the highest predictive accuracy with an R&sup2; value of 1.000. These findings establish TD as the optimal drying conditions for industrial applications, providing a scientific framework for improving BT processing efficiency.

Comprehensive Study on the Drying Kinetics and Mathematical Modelling of Black Turmeric (Curcuma caesia) using Different Drying Methods

Background: Wheat is an important crop of India That is grown in the winter season (October to April). A field study was conducted for nine plots having a dimension of 3 &times; 2 meter and different nutrient was applied to test the biomass yield. The objectives are to describe (a): To conduct field experiments under controlled conditions and vary the dosage of fertilizers for different treatments. (b): To observe the variation in crop yield of wheat with different dosages of fertilizers. (c): To model the crop yield of different treatments using InfoCrop and DSSAT crop models. 
 
Methods: An experiment was conducted for Wheat crop having a dimension of 3 &times; 2 meter at the Indian Institute of Technology, UK-India. Three plots (C1, C2, C3) was given control fertilizer supply while plot (F1, F2, F3) given -25%, +25%, +50% and plot (N1, N2, N3) given -25%, +25%, +50% w.r.t to control. The simulation model Infocrop and DSSAT were simulated to check test the model. 
 
Result: In this study, two crop simulation models namely Infocrop and DSSAT were used to predict the yield of wheat crops. Field experiments were carried out at a farm site at Roorkee, India and different treatments of fertilizers are applied to the field plots. In total, seven fertilizer treatments are used: C (controlled with a normal dosage of fertilizers), F1, F2, F3 (varying amount of applied farmyard manure), N1, N2 and N3 (varying amount of applied urea). The objective of the study was to evaluate yields of the seven treatments using Infocrop and DSSAT crop models and compare them to the observed values of yields. The grain yield of the controlled treatment (C1, C2, C3) was predicted accurately by both models. There was a significant variation in the grain yields of the F1, F2, F3 treatments with the application of farm yard manure (FYM). DSSAT under-predicted the grain yields for the plot F1, F2, F3 treatments and over-predicted these for N1, N2, N3 treatments. Overall, Infocrop, which was developed and validated for Indian conditions, simulated the grain yield more accurately as compared to the DSSAT model under varying applications of FYM and urea dosages.

Wheat Yield Modelling using Infocrop and DSSAT Crop Simulation Models

<div>Background: A reliable method for controlling the sex of farm animals has the potential to revolutionize dairy farming. This can only be achieved by use of a method capable of separating X and Y chromosome bearing spermatozoa at lower cost and causing no significant damages to sperm viability. Realizing significance of buffaloes in Indian dairy farming, present study was aimed to develop appropriate density gradient model for enrichment of X chromosome bearing spermatozoa population in semen of Murrah buffalo bull.&nbsp; 
 
Methods: Density gradient centrifugation (DGC) technique was employed for enriching X chromosome spermatozoa in buffalo bull semen using four gradient media viz., Percoll, Optiprep, Ficoll and Sucrose. Percentage enrichment of X chromosome bearing spermatozoa in all the DGC models was determined by SYBR green based Real Time PCR. 
 
Result: Our investigations revealed that number of layers and centrifugation speed (g) factors in a density gradient centrifugation model, have significant effect on the percentage enrichment of X chromosome content in semen samples.Three layers, four layers and five layers density gradient centrifugation models showed significant differences (P&lt;0.05) in the sex ratio towards X spermatozoa population in semen (61.72%&plusmn;0.81, 64.55%&plusmn;0.26 and 67.31&plusmn;0.33%) respectively. Density gradient centrifugation models with centrifugation speed (g) 200x g and 300x g also showed significant differences (P&lt;0.05) in the sex ratio towards X spermatozoa population in semen (66.36%&plusmn;0.25 and 62.69&plusmn;0.22%) respectively. Out of all DGC models, Optiprep density gradient model with 5 layers and centrifugation at 200xg (O3) yielded maximum enrichment (72.4%&plusmn;1.38). In conclusion, O3 poses to be a promising model for enrichment of X spermatozoa in buffalo bull semen.</div>

<div>Five Layered Optiprep based Density Gradient Model is a Promising Model for Enrichment of Viable X Chromosome Bearing Spermatozoa in Bubalus bubalis</div>

<div>Background: The determination of the soil moisture characteristic is a tedious, lengthy and costly laboratory methods. However, for the specific soil texture, once it is known, the soil moisture at various tension can be simulated through modelling approach which eliminates the requirements of performing the experiments every time. Therefore, the attempt was made for the determinations of the soil moisture characteristics during the year-2019 and validating the various models&rsquo; using these empirical data. 
&nbsp;&nbsp; 
Methods: The present research covers the experimentation during the year 2019 for determination of the soil moisture characteristics and fitting the obtained empirical data to mathematical frames of the various models. The moisture characteristics was obtained by measuring different moisture content values at different suction values through two experiments viz. wetting cycle (Absorption phase) and drying cycle (Desorption phase). The desorption curve was obtained by using the pressure plate apparatus and the wetting curve was measured through the capillary rise open tube method. 
&nbsp; 
Result: The saturation, field capacity, permanent wilting, residual moisture content, void ratio and saturated hydraulic conductivity were found as 43.5%v/v, 25.88%v/v, 12.05%v/v, 8.1%v/v. 0.77 and 0.2808 m/day respectively. Among the fitted models, the van Genuchten model was found best fit to observe data to simulate the desorption and sorption phase of the soil moisture retention characteristics. The soil moisture retention characteristics of the sorption and desorption phases indicated a hysteresis of approximately two log cycles.&nbsp;</div>

Validation of Models for Simulating the Soil Moisture Characteristics

<div>Background:&nbsp;Growth of animals is important for milk and meat production. In developing countries record keeping is difficult and usually complete recording cannot be obtained from the cattle farming. Mathematical models are used to predict values from incomplete or partially recorded data and reduces the confusion for calculating the yield. Therefore, a study was conducted to know the growth of cattle by comparing three non-linear models and predicts the mature live weight. 
 
Mehods: The live weight of cattle at 15 day intervals from 15 to 365 days was recorded and calculated the yearly live weight and weight gains of three genotypes (Red Chittagong cattle, Non-descriptive deshi and their cross) of cattle. Three nonlinear model (Brody, Gompertz and von Bertalanffy model) was fitted to weight-age data of 120 female cattle from 15 to 365 days in three locations.&nbsp; 
 
Result: The average live weight and weight gain of three genotype cattle ranging from 198.5 to 207.89 kg and 306 to 361 g/day, respectively. The brody model provides better goodness of fit than other models in all genotypes and Gompertz and the von Bertalanffy model showed better matched between the observed and estimated weights</div>

Nonlinear Models for the Prediction of Yearly Live Weight of Cattle

<div style="text-align: justify;">To full fill the demands of increasing population for agricultural products, increases pressure on land, water, and other natural resources, to fulfils this demands and to take decision about agricultural product requires information about production, productivity &nbsp;of crop also for proper management and carried out intercultural operations &nbsp;need advanced information about crop phenology, pest and diseases presently it is possible to generate such advanced information through well validated crop growth simulation models such as CERES, WOFOST, SUCROS, and APSIM. These models provided such advanced information based on integrate the effects of different factors viz., increasing temperature, soil productivity, also consider changes in climatic on productivity. The present paper describes the review of research work done on crop simulation model as well as covers detailed information about WOFOST model.&nbsp;</div>